Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories

Abstract

A major problem in road network analysis is discovery of important crossroads, which can provide useful information for transport planning. However, none of existing approaches addresses the problem of identifying network-wide important crossroads in real road network. In this paper, we propose a novel data-driven based approach named CRRank to rank important crossroads. Our key innovation is that we model the trip network reflecting real travel demands with a tripartite graph, instead of solely analysis on the topology of road network. To compute the importance scores of crossroads accurately, we propose a HITS-like ranking algorithm, in which a procedure of score propagation on our tripartite graph is performed. We conduct experiments on CRRank using a real-world dataset of taxi trajectories. Experiments verify the utility of CRRank.

References in corpus (4)

• Leaders in Social Networks, the Delicious Case
• Communicability in complex networks
• Efficient routing on complex networks
• Scaling laws in the spatial structure of urban road networks

Cited by in corpus (5)

• Anomaly Detection in Road Networks Using Sliding-Window Tensor Factorization
• Mining Topological Dependencies of Recurrent Congestion in Road Networks
• MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph Fusion
• Learning to Rank Critical Road Segments via Heterogeneous Graphs with Origin-Destination Flow Integration
• Let Trajectories Speak Out the Traffic Bottlenecks